Evaluating Bayesian networks' precision for detecting students' learning styles

摘要: Students are characterized by different learning styles, focusing on types of information and processing this in ways. One the desirable characteristics a Web-based education system is that all students can learn despite their styles. To achieve goal we have to detect how learn: reflecting or acting; steadily fits starts; intuitively sensitively. In work, evaluate Bayesian networks at detecting style student system. The network models aspects behavior while he/she works with Then, it infers his/her styles according modeled behaviors. proposed model was evaluated context an Artificial Intelligence course. results obtained promising as regards detection students' Different levels precision were found for dimensions style.